CommandParameter pcount("count", "InputTypes", "", "", "NameCount-CountGroup", "none", "none",false,false); parameters.push_back(pcount);
CommandParameter pgroup("group", "InputTypes", "", "", "CountGroup", "none", "none",false,false); parameters.push_back(pgroup);
- CommandParameter psearch("search", "Multiple", "kmer-blast-suffix-distance", "kmer", "", "", "",false,false); parameters.push_back(psearch);
+ CommandParameter psearch("search", "Multiple", "kmer-blast-suffix-distance-align", "kmer", "", "", "",false,false); parameters.push_back(psearch);
CommandParameter pksize("ksize", "Number", "", "8", "", "", "",false,false); parameters.push_back(pksize);
- CommandParameter pmethod("method", "Multiple", "bayesian-knn", "bayesian", "", "", "",false,false); parameters.push_back(pmethod);
+ CommandParameter pmethod("method", "Multiple", "wang-knn-zap", "wang", "", "", "",false,false); parameters.push_back(pmethod);
CommandParameter pprocessors("processors", "Number", "", "1", "", "", "",false,false); parameters.push_back(pprocessors);
CommandParameter pmatch("match", "Number", "", "1.0", "", "", "",false,false); parameters.push_back(pmatch);
CommandParameter pmismatch("mismatch", "Number", "", "-1.0", "", "", "",false,false); parameters.push_back(pmismatch);
helpString += "The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n";
helpString += "The classify.seqs command parameters are reference, fasta, name, group, count, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n";
helpString += "The reference, fasta and taxonomy parameters are required. You may enter multiple fasta files by separating their names with dashes. ie. fasta=abrecovery.fasta-amzon.fasta \n";
- helpString += "The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer, blast and distance. The default is kmer.\n";
+ helpString += "The search parameter allows you to specify the method to find most similar template. Your options are: suffix, kmer, blast, align and distance. The default is kmer.\n";
helpString += "The name parameter allows you add a names file with your fasta file, if you enter multiple fasta files, you must enter matching names files for them.\n";
helpString += "The group parameter allows you add a group file so you can have the summary totals broken up by group.\n";
helpString += "The count parameter allows you add a count file so you can have the summary totals broken up by group.\n";
- helpString += "The method parameter allows you to specify classification method to use. Your options are: bayesian and knn. The default is bayesian.\n";
+ helpString += "The method parameter allows you to specify classification method to use. Your options are: wang, knn and zap. The default is wang.\n";
helpString += "The ksize parameter allows you to specify the kmer size for finding most similar template to candidate. The default is 8.\n";
helpString += "The processors parameter allows you to specify the number of processors to use. The default is 1.\n";
#ifdef USE_MPI
helpString += "The gapextend parameter allows you to specify the penalty for extending a gap in an alignment. The default is -1.0.\n";
helpString += "The numwanted parameter allows you to specify the number of sequence matches you want with the knn method. The default is 10.\n";
helpString += "The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy. The default is 0.\n";
- helpString += "The probs parameter shuts off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be shown.\n";
- helpString += "The iters parameter allows you to specify how many iterations to do when calculating the bootstrap confidence score for your taxonomy with the bayesian method. The default is 100.\n";
+ helpString += "The probs parameter shuts off the bootstrapping results for the wang and zap method. The default is true, meaning you want the bootstrapping to be shown.\n";
+ helpString += "The iters parameter allows you to specify how many iterations to do when calculating the bootstrap confidence score for your taxonomy with the wang method. The default is 100.\n";
//helpString += "The flip parameter allows you shut off mothur's The default is T.\n";
helpString += "The classify.seqs command should be in the following format: \n";
helpString += "classify.seqs(reference=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n";
//check for optional parameter and set defaults
// ...at some point should added some additional type checking...
string temp;
- temp = validParameter.validFile(parameters, "ksize", false); if (temp == "not found"){ temp = "8"; }
- m->mothurConvert(temp, kmerSize);
-
temp = validParameter.validFile(parameters, "processors", false); if (temp == "not found"){ temp = m->getProcessors(); }
m->setProcessors(temp);
m->mothurConvert(temp, processors);
search = validParameter.validFile(parameters, "search", false); if (search == "not found"){ search = "kmer"; }
- method = validParameter.validFile(parameters, "method", false); if (method == "not found"){ method = "bayesian"; }
+ method = validParameter.validFile(parameters, "method", false); if (method == "not found"){ method = "wang"; }
+
+ temp = validParameter.validFile(parameters, "ksize", false); if (temp == "not found"){
+ temp = "8";
+ if (method == "zap") { temp = "7"; }
+ }
+ m->mothurConvert(temp, kmerSize);
temp = validParameter.validFile(parameters, "match", false); if (temp == "not found"){ temp = "1.0"; }
m->mothurConvert(temp, match);
m->mothurConvert(temp, iters);
- if ((method == "bayesian") && (search != "kmer")) {
- m->mothurOut("The bayesian method requires the kmer search." + search + "will be disregarded." ); m->mothurOutEndLine();
+ if ((method == "wang") && (search != "kmer")) {
+ m->mothurOut("The wang method requires the kmer search. " + search + " will be disregarded, and kmer will be used." ); m->mothurOutEndLine();
+ search = "kmer";
+ }
+
+ if ((method == "zap") && ((search != "kmer") && (search != "align"))) {
+ m->mothurOut("The zap method requires the kmer or align search. " + search + " will be disregarded, and kmer will be used." ); m->mothurOutEndLine();
search = "kmer";
}
try {
if (abort == true) { if (calledHelp) { return 0; } return 2; }
- if(method == "bayesian"){ classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters, rand(), flip, writeShortcuts); }
+ string outputMethodTag = method + ".";
+ if(method == "wang"){ classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters, rand(), flip, writeShortcuts); }
else if(method == "knn"){ classify = new Knn(taxonomyFileName, templateFileName, search, kmerSize, gapOpen, gapExtend, match, misMatch, numWanted, rand()); }
+ else if(method == "zap"){
+ outputMethodTag = search + "_" + outputMethodTag;
+ if (search == "kmer") { classify = new KmerTree(templateFileName, taxonomyFileName, kmerSize, cutoff); }
+ else { classify = new AlignTree(templateFileName, taxonomyFileName, cutoff); }
+ }
else {
- m->mothurOut(search + " is not a valid method option. I will run the command using bayesian.");
+ m->mothurOut(search + " is not a valid method option. I will run the command using wang.");
m->mothurOutEndLine();
classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, iters, rand(), flip, writeShortcuts);
}
if (RippedTaxName != "") { RippedTaxName += "."; }
if (outputDir == "") { outputDir += m->hasPath(fastaFileNames[s]); }
- string newTaxonomyFile = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + RippedTaxName + getOutputFileNameTag("taxonomy");
- string newaccnosFile = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + RippedTaxName + getOutputFileNameTag("accnos");
+ string newTaxonomyFile = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + RippedTaxName + outputMethodTag + getOutputFileNameTag("taxonomy");
+ string newaccnosFile = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + RippedTaxName + outputMethodTag +getOutputFileNameTag("accnos");
string tempTaxonomyFile = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + "taxonomy.temp";
- string taxSummary = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + RippedTaxName + getOutputFileNameTag("taxsummary");
+ string taxSummary = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + RippedTaxName + outputMethodTag + getOutputFileNameTag("taxsummary");
if ((method == "knn") && (search == "distance")) {
string DistName = outputDir + m->getRootName(m->getSimpleName(fastaFileNames[s])) + getOutputFileNameTag("matchdist");
if (hasCount) {
ct = new CountTable();
ct->readTable(countfileNames[s]);
- taxaSum = new PhyloSummary(baseTName, ct);
+ taxaSum = new PhyloSummary(taxonomyFileName, ct);
taxaSum->summarize(tempTaxonomyFile);
}else {
if (groupfile != "") { group = groupfileNames[s]; groupMap = new GroupMap(group); groupMap->readMap(); }
- taxaSum = new PhyloSummary(baseTName, groupMap);
+ taxaSum = new PhyloSummary(taxonomyFileName, groupMap);
if (m->control_pressed) { outputTypes.clear(); if (ct != NULL) { delete ct; } if (groupMap != NULL) { delete groupMap; } delete taxaSum; for (int i = 0; i < outputNames.size(); i++) { m->mothurRemove(outputNames[i]); } delete classify; return 0; }
projects / mothur.git / blobdiff